1. Field of the Invention
The present invention relates to the use of machine learning methods for identifying web page content that is most likely to produce a desired user action when incorporated into a dynamically-generated web page.
2. Description of the Related Art
Many web sites serve web pages that include one or more web page components (hereinafter referred to as “components”). A given component may, for example, contain content generated by a particular code module or service, and may occupy a particular area or section of a web page. Typically, the components contain links, buttons or other controls for allowing users to perform specific actions, such as adding a displayed item to a shopping cart.
In selecting components to be incorporated into a web page, the web site operator typically wants to present the most effective set of components to the user. The effectiveness of presenting a component can be a measure of whether a desired result is obtained from the user and/or whether a desired action is performed by the user. The desired actions or results can be any action or result an operator of a web site might want to obtain from a user. For example, desired actions for an advertiser-supported on-line content provider might be, for example, the selection of a banner advertisement by a user or selection of a hypertext link to another page of the web site on which additional banner advertisements are displayed.
In accordance with existing techniques, in order to determine the effectiveness of presenting a component to a user, web site operators manually set up tests in which components are presented to users and activity resulting from presenting the components is tracked. The tracked activity can include any user activity of interest resulting from presenting the component to a user, such as a selection of a hypertext link included in the component, or an addition of a product displayed or represented by the component to a shopping cart or a wish list. The tests are typically conducted in such a way that users are not aware that they are the subject of a test of the effectiveness of a component. Based upon analysis of the resulting activity, the effectiveness of components can be determined. Determinations as to which components to present to users can then be based upon the determined effectiveness of the tested components.
The use of manual tests in determining the effectiveness of components has several drawbacks. Conducting manual tests in order to determine the effectiveness of components is very labor intensive. Due to this labor intensive nature, the number of tests and the level of detail of the tests are limited by available manpower. In addition, these tests typically also do not take into consideration differing tastes or preferences among numerous particular types or classes of users. Manual tests also typically have a finite duration so that new tests must be conducted as new components are introduced and user trends change. Furthermore, once the results of a test are obtained, human intervention is typically required in order to propagate the results into a change in components that are displayed. The present invention seeks to address these deficiencies, among others.